As predictive modeling becomes ubiquitous across a wide swath of application areas, it is especially critical to understand which variables contribute most to making a particular prediction. Black-box machine learning methods are insufficient in the face of algorithmic decision-making about things like sentencing, healthcare, and education, and working toward developing more interpretable methods is becoming more and more relevant [1]}, [2]}.
